基于支持向量机分类策略的多晶硅电池片色差检测

doi:10.3969/j.issn.1000-1158.2019.06.12

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Abstract Aiming at the problem of color difference detection on polycrystalline silicon cells under complex color and texture characteristics, a new method based on support vector machine classification strategy is proposed to detect the color difference of polycrystalline silicon cells. Firstly, color model conversion and channel separation are performed on the pre-processed cell images. The Otsu method is used to perform threshold segmentation processing on the single-channel image, and the region contrast of each threshold image is calculated, and then an appropriate threshold image is selected according to the regional contrast condition. The image features are extracted by the information provided by the threshold image. Finally, the support vector machine classifier is used to determine whether the cell has a color difference defect. The experimental results show that the proposed color difference detection algorithm can achieve high-efficiency detection of color difference defect, and the accuracy, false detection rate and detection time reach 96.88%, 5% and 109 ms.

Key words： metrology polycrystalline silicon cell color difference detection image segmentation regional contrast machine vision support vector machine classification

Received: 14 June 2019 Published: 10 October 2019

PACS:	TB96
	TP391.41

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	Articles by authors
	GUO Bao-su
	WU Wen-wen
	FU Qiang
	WU Feng-he

Cite this article:

GUO Bao-su,WU Wen-wen,FU Qiang, et al. Color Difference Detection of Polycrystalline Silicon Cells Based on Support Vector Machine Classification Strategy[J]. Acta Metrologica Sinica, 2019, 40(6): 1013-1019.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.06.12 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I6/1013

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